Exploiting the gain-modulation mechanism in parieto-motor neurons: Application to visuomotor transformations and embodied simulation

Gated spiking neural network using Iterative Free-Energy Optimization and rank-order coding for structure learning in memory sequences (INFERNO GATE)

We present a framework based on iterative free-energy optimization with spiking neural networks for modeling the fronto-striatal system (PFC-BG) for the generation and recall of audio memory sequences. In line with neuroimaging studies carried out in the PFC, we propose a genuine coding strategy using the gain-modulation mechanism to represent abstract sequences based solely on the rank and location of items within them. Based on this mechanism, we show that we can construct a repertoire of neurons sensitive to the temporal structure in sequences from which we can represent any novel sequences. Free-energy optimization is then used to explore and to retrieve the missing indices of the items in the correct order for executive control and compositionality. We show that the gain-modulation mechanism permits the network to be robust to variabilities and to have long-term dependencies as it implements a gated recurrent neural network. This model, called Inferno Gate, is an extension of the neural architecture Inferno standing for Iterative Free-Energy Optimization of Recurrent Neural Networks with Gating or Gain-modulation. In experiments performed with an audio database of ten thousand MFCC vectors, Inferno Gate is capable of encoding efficiently and retrieving chunks of fifty items length. We then discuss the potential of our network to model the features of working memory in the PFC-BG loop for structural learning, goal-direction and hierarchical reinforcement learning.

Brain-Inspired Coding of Robot Body Schema Through Visuo-Motor Integration of Touched Events

Representing objects in space is difficult because sensorimotor events are anchored in different reference frames, which can be either eye-, arm-, or target-centered. In the brain, Gain-Field (GF) neurons in the parietal cortex are involved in computing the necessary spatial transformations for aligning the tactile, visual and proprioceptive signals. In reaching tasks, these GF neurons exploit a mechanism based on multiplicative interaction for binding simultaneously touched events from the hand with visual and proprioception information.By doing so, they can infer new reference frames to represent dynamically the location of the body parts in the visual space (i.e., the body schema) and nearby targets (i.e., its peripersonal space). In this line, we propose a neural model based on GF neurons for integrating tactile events with arm postures and visual locations for constructing hand- and target-centered receptive fields in the visual space. In robotic experiments using an artificial skin, we show how our neural architecture reproduces the behaviors of parietal neurons (1) for encoding dynamically the body schema of our robotic arm without any visual tags on it and (2) for estimating the relative orientation and distance of targets to it. We demonstrate how tactile information facilitates the integration of visual and proprioceptive signals in order to construct the body space.

Spatio-Temporal Tolerance of Visuo-Tactile Illusions in Artificial Skin by Recurrent Neural Network with Spike-Timing-Dependent Plasticity

Perceptual illusions across multiple modalities, such as the rubber-hand illusion, show how dynamic the brain is at adapting its body image and at determining what is part of it (the self) and what is not (others). Several research studies showed that redundancy and contingency among sensory signals are essential for perception of the illusion and that a lag of 200-300 ms is the critical limit of the brain to represent one's own body. In an experimental setup with an artificial skin, we replicate the visuo-tactile illusion within artificial neural networks. Our model is composed of an associative map and a recurrent map of spiking neurons that learn to predict the contingent activity across the visuo-tactile signals. Depending on the temporal delay incidentally added between the visuo-tactile signals or the spatial distance of two distinct stimuli, the two maps detect contingency differently. Spiking neurons organized into complex networks and synchrony detection at different temporal interval can well explain multisensory integration regarding self-body.